The ability to accurately control motion of a structure in three-dimensional space, or to control motion of one structure relative to another structure in a given space, poses a problem of significant technological and economic consequence to many manufacturing applications, such as those used to fabricate semiconductor chips, printed circuit boards, liquid crystal displays, and thin film devices. Such operations often employ specialized structures, such as lithography stages, laser light sources, metrology stages, pick-and place-equipment, wafer-handling robots, gantry/head assemblies, linear motors, photoimaging systems, and etching systems, to manufacture and inspect these often-delicate and sensitive products.
Indeed, semiconductor chip manufacture can be so sensitive, that tiny ranges of unwanted motion, for example, in the micrometer (xcexcm) to nanometer (nm) range, can interfere with components or subsystems that require precise alignment and positioning. The need for such near-exacting precision in chip manufacturing is illustrated, for instance, in the careful matching of a wafer mask to a silicon substrate. Because small variances in mask placement may escape detection until the quality control inspection, until installation in end-products, the need for identifying and quickly correcting the effect of positioning and disturbance-related errors in the first place is of utmost importance.
These structures may be required to move very quickly to specific points in space, whether to an absolute coordinate or to a point defined in relation to another point, such as another structure. A wafer stage is a moveable structure in a lithography machine which carries wafers and positions them for illumination. A stage carrying a wafer must move to precisely aligned points with respect to an optical lens so that an image may be created on the wafer using photolithographic processes. The image is then further processed to create the fine circuitry within semiconductor devices. These processes are generally repeated multiple times creating layers of the fine circuitry. Alignment of these layers can be critical to the performance of the devices. Alignment errors of several nm can sometimes be sufficient to render a device useless or severely limit its performance. Because structures such as these typically operate within specifically calibrated, relatively fault-intolerant operational ranges of movement, and because the movements themselves must be precisely and efficiently timed and executed, methods and devices which allow for precise and optimized control would present a welcome opportunity to improve such manufacturing and inspection processes.
As chip-making technology has advanced, for example, through the use of advanced photolithography lasers such as those sold by Cymer, Inc. of San Diego, Calif., chip throughput requirements have also increased. One consequence of the increased requirements has been a faster positioning of the stages on which the reticules and wafers ride. With faster positioning has come dramatic increases in the attendant motion control issues. For example, among other effects upon manufacturing, faster positioning has created a need to predict and control flexible deformation modes of stages. This, coupled with the relatively low level of structural damping of typical stages (arising from the requirement that the stages be both light and stiff), creates a host of scenarios where stages must be carefully controlled to achieve sustained, near-optimal operational behavior.
Active vibration and motion control provides one promising method of achieving adequate system governance. However, unknowns in plant dynamics and unforeseen disturbances to systems being controlled can significantly alter the actual results attained through active structural control, especially when used with sensitive machines such as semiconductor capital equipment. In this context, disturbances can manifest themselves in a variety of ways, such as affecting the signals input to the system being controlled, causing variances in sensor signals or by impacting performance variables. In addition, uncertainty in base or stage dynamics, and the impact upon those dynamics caused by changes in equipment configuration, mass distribution, and aging of equipment, subsystems, or components, all may serve to limit the performance of any standard control method chosen.
In order to achieve required precision lithography stages are supported by base structures that are actively isolated from floor vibrations. Active isolation control in its simplest form requires that these bases be extremely heavy with respect to the stages, and that they have no structural elastic behavior in the active isolation control band. These requirements lead to such bases often weighing several metric tons, and being constructed of difficult to use materials such as granite. This significantly increases the cost of these bases, as well as associated costs such as handling, transportation, etc. It also requires that actuators used in the system have much higher force ratings, and therefore also be expensive and difficult to handle, as well as requiring large space for amplifiers and other components. Prior art active isolation control systems typically use a simple low order single input single output (SISO) control algorithms.
SISO control algorithms impose these severe limitations on base structures for a number of reasons including:
1. Control of multiple axes with SISO controllers requires efficient and robust decoupling of the motion along the different axes (plunge, pitch, roll, etc.). Such decoupling cannot be achieved when center of gravity and rotary inertias change as a function of time as they would if the mass ratio of moving stage to base was not low in the extreme.
2. Even in the absence of any moving mass, axes of motion decoupling is only possible when the base behaves as one rigid body (below the first resonant frequency). If the base has elastic vibrations in the isolation control band, no decoupled SISO control is possible.
3. Low-order controllers cannot address lightly damped structural vibrations because they have limited gain roll-off values (typically 20 dB per decade), and therefore the presence of such vibrations at or even near the desired control band will severely limit performance of such low-order controllers.
Thus the active isolation system is limited in its performance and requires extremely cumbersome mechanical design. Additionally, the low-order SISO controllers in current practice must be tuned by highly trained, technical personnel.
The shortcomings of active control are especially appreciated when taken from a thoroughly predictable laboratory setting to the rigors of the factory floor. In laboratory tests, one can completely characterize the system being controlled, including experimentally induced disturbances, before closing the loops and then adjust the control gains to get the best possible response out of the system. In this manner, it is possible to eliminate nearly all of the uncertainty about a system""s input/output behavior in a specified frequency range, especially when using modern system identification techniques. In real world applications, however, it is often impossible to recreate system performance identical to that observed in the lab. Part-to-part variation results in significant differences in response to control inputs, even between nominally identical systems, and even when using the same controller. Changes in environment and equipment configuration can cause even more insidious (and difficult to pinpoint) modeling errors because they can vary from location to location and may also vary with time. These issues invariably arise in the case of semiconductor fabrication equipment, where the dynamics of the individual system cannot be completely known until it has been deployed and used in the factory. Furthermore, the exact character of a disturbance in physical conditions, let alone specific disturbance frequencies, are rarely known ahead of time with the precision needed to optimize performance and, unfortunately, can be time-varying themselves.
Researchers have been addressing these issues outside of the semiconductor industry by applying adaptive control techniques to the structural control problem. The thrust of these efforts has been to make the adaptive control algorithms as general as possible, with the goal of making a controller which uses an unchanging theoretical model to work for all conceivable systems under all conditions. Such a control algorithm necessarily (and undesirably) complex and, for most practical applications limits the performance of the controller.
Some research in the area of adaptive control (see xc3x85strxc3x6m, K. J; Wittenmark, B.; Adaptive Control, Addison-Wesley Publishing Company, 1995, and Narendra, K. S.; Annaswamy, A. M.; Stable Adaptive Systems, Prentice-Hall Inc., Englewood Cliffs, N.J., 1989) has focused on its application to flexible structures. Roughly, the favored approaches of these efforts can be divided into three classes of feedback control: direct adaptive control, self tuning regulators, and tonal controllers. The direct adaptive controllers compute control gains xe2x80x9cadaptivelyxe2x80x9d, i.e., directly from measurement errors. (See Annaswamy, A. M.; Clancy, D. J.; xe2x80x9cAdaptive control strategies for flexible space structuresxe2x80x9d, IEEE Transactions on Aerospace and Electronic Systems, v32 n3, July 1996; Bakker, R.; Annaswamy, A. M.; xe2x80x9cLow-order multivariable control with application to flexible structuresxe2x80x9d, Automatica v32 n3, March 1996; and Ho, M-T Yang, J. C.; Chew, M.; xe2x80x9cNew adaptable reference model adaptive control for slewing control of a flexible beam with an unknown tip loadxe2x80x9d, Proceedings of the SPIE Smart Structures and Materials Conference: Smart Structures and Integrated Systems, v2443, February-March 1995.) Tonal controllers are those designed to perform disturbance rejection at one or several discrete frequencies. (See Yen, G. G.; xe2x80x9cActive vibration control in precision structuresxe2x80x9d, Proceedings of the SPIE Conference on Artificial Neural Networks III, v3077, April 1997; Boson, M.; Douglas, S. C.; xe2x80x9cNarrowband disturbance rejection using adaptive feedback algorithmsxe2x80x9d, Proceedings of the SPIE Smart Structures and Materials Conference: Mathematics and Control in Smart Structures, v 3039, March 1997; and Bodson, M.; Douglas, S. C.; xe2x80x9cRejection of disturbances with a large sinusoidal component of unknown frequencyxe2x80x9d Proceedings of the SPIE Smart Structures and Materials Conference: Mathematics and Control in Smart Structures, v2715, February 1996.) The disturbance is a sinusoid, usually of unknown frequency. The tonal controller either adapts to changes in frequency, changes in plant dynamics, or both. This type of control can achieve perfect disturbance rejection (as measured by the sensors) in instances where the number of error sensors is less than or equal to the number of actuators and the actuators have sufficient control authority. Self tuning regulators add an extra step to the adaptation process, namely, the adaptive updating of an internal model in the tuning algorithm. This model is used to compute control gains. These methods do not generally require collocation, and are distinguished from each other primarily by the algorithm used to perform identification (ID) of the internal model. Among the ID methods used in these types of controllers are neural nets, (see for example, Davis, L. D.; Hyland, D. C.; xe2x80x9cAdaptive neural control for the ASTREX testbedxe2x80x9d, Proceeding of the American Control Conference, v3, June 1997, modal parameters (see for example, Baz, A.; Hong, J-T.; xe2x80x9cAdaptive control of flexible structures using modal positive position feedbackxe2x80x9d, International Journal of Adaptive Control and Signal Processing, v11 n3, May 1997) physical structural properties (e.g. mass and stiffness) (see Gopinathan, M.; Pajunen, G. A.; Neelakanta, P. S.; Arockiasamy, M.; xe2x80x9cLinear quadratic distributed self-tuning control of vibration in a cantilever beamxe2x80x9d, Proceedings of the SPIE Smart Structures and Materials Conference: Smart Structures and Integrated Systems, v2443, February-March 1995) and families of models that span the parameter variation space (see Fitch, J. A.; Maybeck, P. S.; xe2x80x9cMultiple model adaptive control of a large flexible space structure with purposeful dither for enhanced identifiabilityxe2x80x9d, Proceeding of the 33rd IEEE Conference on Decision and Control, v3, December 1994; and Schiller, G. J.; Maybeck, P. S.; xe2x80x9cControl of a large space structure using MMAE/MMAC techniquesxe2x80x9d, IEEE Transaction s on Aerospace Electronic Systems, v33 n4, October 1997).
What is needed is a better base stabilization system.
The present invention provides a base stabilization system for controlling motion of a controlled structure. The system includes a ground structure such as the floor of a fabrication facility and the controlled structure includes a base on which equipment is mounted. The system also includes at least three air mounts and a plurality of actuators all attached to the ground structure and to the base to isolate the base from the ground structure and to stabilize the base.
The system includes a plurality of position and acceleration sensors each of which are co-located with a corresponding actuator. The system also includes a multi-input, multi output feedback control system comprising a computer processor programmed with a feedback control algorithm for controlling each of the actuators based on feedback signals from each of the sensors. The co-location of the sensors with the actuators avoids serious problems resulting from higher order vibration modes.
In a preferred embodiment the base stabilization system is applied to an integrated circuit lithography scanner machine. The air mounts support a base of the scanner machine. Actuators are provided at each air mount to control motion in the vertical z direction. Two additional actuators are provided to control horizontal motion in a y direction and one actuator controls motion in an x direction.
Position and acceleration sensors are co-located with each actuator and based on signals form the sensors the base and the scanner machine supported by it are stabilized by actuators which are controlled by a control system which includes a computer processor programmed with a feedback control algorithm developed using a linear quadratic regulator approach.